Study of Abuse Detection in Continuous Speech for Indian Languages

The presence of abusive content on social media platforms poses a significant challenge in maintaining a positive online environment for millions of users. While automatic abuse detection has seen extensive use in the text domain, audio abuse detection remains relatively unexplored. This paper addresses the modeling challenges inherent in identifying offensive content within real-life audio recordings, particularly in the multilingual context of two Indian languages. We introduce a cascaded model that combines an automatic speech recognition system with textual keyword spotting and compare it with an end-to-end model utilizing audio-level feature embeddings and neural classifiers. Our findings, based on the ADIMA dataset, demonstrate that both methods achieve similar discriminability, but the cascaded linguistic approach stands out for its enhanced explainability and the potential to leverage well-established Natural Language Processing techniques for abuse detection.